Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors

被引：0
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作者：
Ya-Ting Jan
Pei-Shan Tsai
Wen-Hui Huang
Ling-Ying Chou
Shih-Chieh Huang
Jing-Zhe Wang
Pei-Hsuan Lu
Dao-Chen Lin
Chun-Sheng Yen
Ju-Ping Teng
Greta S. P. Mok
Cheng-Ting Shih
Tung-Hsin Wu
机构：
[1] National Yang Ming Chiao Tung University,Department of Biomedical Imaging and Radiological Sciences
[2] MacKay Memorial Hospital,Department of Radiology
[3] MacKay Medical College,Department of Medicine
[4] MacKay Junior College of Medicine,Division of Endocrine and Metabolism, Department of Medicine
[5] Nursing and Management,Department of Radiology
[6] Taipei Veterans General Hospital,School of Medicine
[7] Taipei Veterans General Hospital,Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology
[8] National Yang Ming Chiao Tung University,Department of Biomedical Imaging and Radiological Science
[9] University of Macau,undefined
[10] China Medical University,undefined
来源：
Insights into Imaging | / 14卷
关键词：
Ovarian tumor; Radiomics; Deep learning; Machine learning; Computed tomography;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
CT-based radiomics and deep learning features could differentiate ovarian tumors.Radiomics, deep learning features, and clinical data provided complementary tumor information.The ensemble model improved the radiologists’ performance in assessing ovarian tumors.
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